Article Text

Download PDFPDF
095 Artificial Intelligence based detection of Parkinson’s disease in magnetic resonance imaging brain scans
  1. Megan Courtman1,
  2. Mark Thurston2,
  3. Lucy McGavin2,
  4. Camille Caroll1,
  5. Lingfen Sun1,
  6. Emmanuel Ifeachor1,
  7. Stephen Mullin1
  1. 1University of Plymouth
  2. 2University Hospital Plymouth NHS Trust

Abstract

Background There is a need for diagnostic tests of early PD diagnosis. A subset of AI known as deep learning (DL) has shown great promise in diagnostic medical imaging, sometimes outperforming radiolo- gists by detecting patterns invisible to the human eye. Using DL, we explored whether such changes are detectable on routine PD MRI scans.

Methods We trained a convolutional neural network to classify 138 PD and 60 control brain MRI images acquired from the Parkinson’s Progression Marker Initiative (PPMI) database. Models were assessed using k-fold cross-validation. We used Deep SHapley Additive exPlanations (DeepSHAP) to visualise the contri- bution of individual pixels to the model’s prediction.

Results A combined dataset of axial T2 and proton density MRI images was classified with 79% accuracy and an area under the curve (AUC) of 0.86. Respectively T2 and proton density models classified cases with 81/84% accuracy and AUC of 0.83/0.88. DeepSHAP heat maps demonstrated predominant interest in midbrain slices.

Conclusion Our models exhibited good diagnostic performance and demonstrated interest in PD relevant brain regions. We will validate this model in a large dataset of routinely collected NHS MRI scans, many of which predate onset of motor symptoms.

Statistics from Altmetric.com

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.